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中文摘要：
针对多标签数据类别间的相关性与共现性，提出了一种使用自适应线性回归的多标签分类算法，将经典线性回归理论推广到多标签形式，结合多种评判标准对回归结果设置阈值，进而自适应地预测出最终标签．该方法同时考虑了符合数据期望的固定阈值与反映分类器综合效果的自适应阈值，因而降低了数据分布与噪声对分类的影响．实验结果表明，该方法可以有效地解决多标签分类问题．
英文摘要：
Aiming at the co-occurrence and relevance among the multi-label data, a novel multi-label classification algorithm using adaptive linear regression is proposed. In the algorithm, first, the classical linear regression theory is extended to the muhi-label linear regression. Then, the threshold for the regression results is set by combining various evaluation criteria, thus adaptively predicting the final labels. The proposed algorithm considers not only the fixed threshold corresponding to the averages but also the adaptive thresholds reflecting the comprehensive effects of the classifier, thus reducing the influence of the distribution and noise of original data on the classification. Experimental results demonstrate that the proposed algorithm is effective in the multi-label classification.
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